Image denoising is a fundamental task in low-level computer vision. While recent deep learning-based image denoising methods have achieved impressive performance, they are black-box models and the underlying denoising principle remains unclear. In this paper, we propose a novel approach to image denoising that offers both clear denoising mechanism and good performance. We view noise as a type of image style and remove it by incorporating noise-free styles derived from clean images. To achieve this, we design novel losses and network modules to extract noisy styles from noisy images and noise-free styles from clean images. The noise-free style induces low-response activations for noise features and high-response activations for content features in the feature space. This leads to the separation of clean contents from noise, effectively denoising the image. Unlike disentanglement-based image editing tasks that edit semantic-level attributes using styles, our main contribution lies in editing pixel-level attributes through global noise-free styles. We conduct extensive experiments on synthetic noise removal and real-world image denoising datasets (SIDD and DND), demonstrating the effectiveness of our method in terms of both PSNR and SSIM metrics. Moreover, we experimentally validate that our method offers good interpretability.

翻译：图像去噪是低级计算机视觉中的基本任务。尽管近期基于深度学习的图像去噪方法取得了令人瞩目的性能，但这些模型属于黑箱模型，其内在的去噪原理尚不明确。本文提出了一种兼具清晰去噪机制与优良性能的新型图像去噪方法。我们将噪声视为一种图像风格，并通过融入来自干净图像的无噪声风格来去除噪声。为实现这一目标，我们设计了新的损失函数和网络模块，用于从带噪图像中提取含噪风格，并从干净图像中提取无噪声风格。在特征空间中，无噪声风格能抑制噪声特征的响应，同时增强内容特征的响应，从而将干净内容与噪声分离，实现有效的图像去噪。与基于解耦的图像编辑任务（通过风格编辑语义级属性）不同，本文的核心贡献在于通过全局无噪声风格编辑像素级属性。我们在合成噪声去除和真实图像去噪数据集（SIDD和DND）上进行了大量实验，结果表明我们的方法在PSNR和SSIM指标上均具有优越性。此外，通过实验验证，我们的方法具有良好的可解释性。